Abstract

This thesis explores and analyzes case studies for the application of algorithms in curatorial practice. It takes on a broad definition of both the meaning of algorithm and curating with aims to provide valuable insight on ways to understand and develop curatorial practice.

This open approach tries to include the wide range of meanings both of these terms have given their incremental presence in our daily tasks. The thesis develops along four concepts: algorithms as linear and sequential, curating as sequential algorithm, algorithms as systems, and curating as systems. By focusing on both linear and systemic processes curating can be diversified in its scope, understanding and efficiency.

The first part of the thesis is concerned with the understanding of algorithm as series of steps to be executed, and how this type of algorithm can be traced within the field of curating. It delves into cases of conceptual art exhibition making in the 60’s, and their algorithmic nature despite being carried out by humans. In terms of studying how sequential tasks carried out by machines can be applied in the field of curating, the thesis focuses in the implementation of algorithmic search of the web as part of curatorial criteria for museums (MuDA Museum in Zurich), as well as the implementation of machine learning tools applied to the analysis and processing of image and text data (re][cognition project in Tate or Google X degrees of separation), and how that information can be used in the curatorial process. Besides these historic and theoretical components, the thesis also includes a practical implementation in the shape of an online exhibition, Expanded Archive.

The second part analyzes how linear or individual algorithms are organized, assembled and stacked into systems. It explores the work of curators and researchers Joasia Krysa and Magdalena Tyzlik-Carver as means to approach to the notions of network, entanglement and assemblage, in order to be able to describe how the environment of a curatorial and informatic system is laid out. This section also analyzes how the content of datasets affect algorithmic systems, with the goal of drawing an analogy to understand the importance of data sourcing in curatorial practice; highlighting that the way how we organize data, where do we source it from, and what we take as its criteria constitute crucial parameters for curating. This will provide insights on the limits of what can be measured and represented in digital form and what not, allowing us to better clarify the limits that computer-based curating tools have.

The goal of this research is to find a shared historical background for algorithms and curating, find useful applications of the former into the latter, contextualize curating within the rise of algorithmic proceduralism in most fields of knowledge, as well as expanding both notions in ways that can be useful for creative and critical production in art.